DoS攻击下具备隐私保护的多智能体系统均值趋同控制

均值趋同是一种广泛应用于分布式计算和控制的算法, 旨在系统通过相邻节点间信息交互、更新, 最终促使系统中所有节点以它们初始值的均值达成一致. 研究拒绝服务(Denial-of-service, DoS)攻击下的分布式离散时间多智能体系统均值趋同问题. 首先, 给出一种基于状态分解思想的分布式网络节点状态信息处理机制, 可保证系统中所有节点输出值的隐私. 然后, 利用分解后的节点状态值及分析给出的网络通信拓扑条件, 提出一种适用于无向通信拓扑的多智能体系统均值趋同控制方法. 理论分析表明, 该方法能够有效抵御DoS攻击的影响, 且实现系统输出值均值趋同. 最后, 通过仿真实例验证了该方法的有效性.

Privacy-preserving Average Consensus Control for Multi-agent Systems Under DoS Attacks

Average consensus is a widely used algorithm for distributed computing and control, where all the nodes in the network constantly communicate and update their states in order to achieve an agreement. In this paper, we study the average consensus problem for discrete-time multi-agent systems under DoS attacks. First, a distributed network node state value processing mechanism based on state decomposition is given, which can ensure the privacy of the output values of all nodes in the system. Then, through using the decomposed node state values and the network topology conditions given by the analysis, an average output consensus control law for distributed discrete-time multi-agent systems is proposed. Theoretical analysis shows that the proposed method can effectively resist the influence of DoS attacks on the system, and achieve the convergence of the average value of system initial outputs. Finally, numerical examples are presented to show the validity of the proposed method.